Table 4.

Comparison of Test Accuracy and AUC With State-of-the-Art EF Classifiers

Model Metric	Classification Approach
	Our Approacha	Asch et al7b	Ouyang et alc	Almadani et al8d	Muldoon and Khan17e
Accuracy	0.875	0.92	N/A	0.902	0.87
AUC	0.916	N/A	0.97	0.847	N/A
EF classification cutoff	50%	35%	50%	50%	50%

Our model performance approaches current SOTA (state-of-the-art) classifiers developed for the same EF classification problem, underscoring its quality. Our accuracy is higher than that of the latest model, for instance, while coming within 5 points of the best SOTA accuracy. Our AUC is also higher than that of the latest model and comes within 6 points of the best SOTA AUC.

AUC = area under the receiver-operating characteristic curve; EF = ejection fraction; GSM = gate shift module.

^aR3D transformer, ResNet18 backbone.

^bUndisclosed algorithm.

^c3D convolutional neural network with atrous convolutions.

^dGSM, inception backbone, 32-frame echocardiograms.

^eMobile U-Net.